For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions -- about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.

A Recurrent Neural Network or RNN is a popular multi-layer neural network that has been utilised by researchers for various purposes including classification and prediction. The applications of this network include speech recognition, language modelling, machine translation, handwriting recognition, among others. The recurrent neural network is an interesting topic and what's more about this article is that all the courses mentioned here are free to learn. Below here, we listed down the top 10 free resources, in no particular order, to learn recurrent neural networks (RNNs). About: Here, you will understand how to implement recurrent neural networks (RNNs).

Online Courses Udemy - Data Analytics: SQL for newbs, beginners and marketers, Dominate data analytics, data science, and big data Created by Lazy Programmer Inc English [Auto-generated] Students also bought Data analyzing and machine learning Hands-on with KNIME Machine Learning Practical: 6 Real-World Applications Careers in Data Science A-Z Statistics Masterclass for Data Science and Data Analytics Text Mining and Natural Language Processing in R Preview this course GET COUPON CODE Description It is becoming ever more important that companies make data-driven decisions. With big data and data science on the rise, we have more data than we know what to do with. One of the basic languages of data analytics is SQL, which is used for many popular databases including MySQL, Postgres, SQLite, Microsoft SQL Server, Oracle, and even big data solutions like Hive and Cassandra. I'm going to let you in on a little secret. Most high-level marketers and product managers at big tech companies know how to manipulate data to gain important insights.

With the success of DeepMind's AlphaGo system defeating the world Go champion, reinforcement learning has achieved significant attention among researchers and developers. Deep reinforcement learning has become one of the most significant techniques in AI that is also being used by the researchers in order to attain artificial general intelligence. Below here is a list of 10 best free resources, in no particular order to learn deep reinforcement learning using TensorFlow. About: This tutorial "Introduction to RL and Deep Q Networks" is provided by the developers at TensorFlow. The topics include an introduction to deep reinforcement learning, the Cartpole Environment, introduction to DQN agent, Q-learning, Deep Q-Learning, DQN on Cartpole in TF-Agents and more.

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right? You've found the right Convolutional Neural Networks course! Identify the Image Recognition problems which can be solved using CNN Models. Create CNN models in Python using Keras and Tensorflow libraries and analyze their results. Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.

Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.

Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

It is common to aggregate information and evaluate items by collecting ratings on these items from people. In this work, we focus on the bias introduced by people's observable outcome or experience from the entity under evaluation, and we call it the "outcome-induced bias". Let describe this notion of bias with the help of two common applications - teaching evaluation and peer review. Many universities use student ratings for teaching evaluation. However, numerous studies have shown that student ratings are affected by the grading policy of the instructor [16, 26, 5]. For instance, as noted in [26, Chapter 4]: "...the effects of grades on teacher-course evaluations are both substantively and statistically important, and suggest that instructors can often double their odds of receiving high evaluations from students simply by awarding A's rather than B's or C's." As a consequence, the association between student ratings and teaching effectiveness can become negative [5], and student ratings serve as a poor predictor on the follow-on course achievement of the students [8, 6]: "...teachers who are associated with better subsequent performance receive worst evaluations from their students."

Are you a programmer who wants to understand how to customize Conversational Chat bots programmatically on the YM platform? In this course, you will learn the core Programming concepts of YM Platform known as Cloud Functions in order to customize your Chat bot. You will write custom logic using Cloud Function – Objects and methods, and test that logic using the built-in testing tool. You will explore how Cloud Functions interacts with UI of the platform. You will get hands-on experience writing code to customize your chat bot interface to support different channels, as well as a brief introduction to the built in Database.

Finland has released a free online artificial intelligence (AI) course which aims to educate public administration, businesses and the general public about the technology and to consider what it should be used for. The Ethics of AI course offered by the University of Helsinki has been designed in a partnership with the cities of Helsinki, Amsterdam and London as well as Finland's Ministry of Finance. Questions pertaining to the ethics of AI are topical, as many people are already making ethical choices in their work, for example, on data use. The course sets out to help them understand what the ethical use of artificial intelligence means and what it requires from both society and individuals. "These questions include how our data is used, who is responsible for decisions made by computers and whether, say, facial recognition systems are used in a way that acknowledges human rights. In a broader sense, it's also about how we wish to utilise advancing technical solutions," said Anna-Mari Rusanen, course coordinator for Ethics of AI.